Wireless power transfer (WPT) provides a convenient method of delivering energy to multiple devices. With the increasing use of WPT, safety concerns inevitably create the need for a reliable control mechanism. Previous approaches in advanced WPT or metamaterial-enhanced WPT, however, have the limitation that neither the intensity nor the shape of the field-localizing area can be dynamically controlled. To address this limitation, we introduce the novel concept of a hotspot or power-focused region using field-localizing WPT. Using the proposed method, we provide experimental evidence demonstrating that the location, shape, and intensity of the hotspot can be manipulated as desired. The hotspot effectively enhances power delivery to the intended device while reducing leakage to unwanted areas. To dynamically reconfigure the hotspots, we propose an active metasurface with multi-functionality due to its frequency switching and tuning capability. The dynamic reconfiguring capability provides a wide range of versatile practical applications, overcoming the limitations associated with passive metamaterials. Because the location, shape, and intensity of hotspots can readily be controlled, the proposed method is not limited to WPT applications. It can also be used for a broad range of applications that require precise control of power delivery.

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Controlling power to an unintended area is an important issue for enabling wireless power transfer (WPT) systems. The control allows us to enhance efficiency as well as suppress unnecessary flux leakage. The flux leakage from WPT can be reduced effectively via selective field localization. To realize field localization, we propose the use of cavities formed on a single metamaterial slab that acts as a defected metasurface. The cavity is formed by strong field confinement using a hybridization bandgap (HBG), which is created by wave interaction with a two-dimensional array of local resonators on the metasurface. This approach using an HBG demonstrates strong field localization around the cavity regions. Motivated by this result, we further investigate various cavity configurations for different sizes of the transmitter (Tx) and receiver (Rx) resonators. Experiments show that the area of field localization increases with the number of cavities, confirming the successful control of different cavity configurations on the metasurface. Transmission measurements of different cavities show that the number of cavities is an important parameter for efficiency, and excess cavities do not enhance the efficiency but increase unnecessary power leakage. Thus, there exists an optimum number of cavities for a given size ratio between the Tx and Rx resonators. For a 6:1 size ratio, this approach achieves efficiency improvements of 3.69× and 1.59× compared to free space and a uniform metasurface, respectively. For 10:1 and 10:2 size ratios, the efficiency improvements are 3.26× and 1.98× compared to free space and a uniform metasurface, respectively.

Wireless power transfer (WPT), a convenient method for powering multiple devices, enables a truly wireless connection, eliminating the need for periodic charging and replacing a battery. To further enhance WPT, the unique characteristics of metamaterial, such as its field focusing and evanescent wave amplification, have been successfully utilized. With subwavelength characteristics, computational challenges arise when the number of metamaterial unit cells is increased. In this work, we investigate a deep neural network (DNN)-based design of the tunable metamaterial for WPT. Using structures specifically designed for different tasks, the DNN predicts the frequency spectra and synthesizes the unit cell's design parameters. When trained using a set of ~23000 randomly selected designs, we achieve an accumulated mean square error (MSE) of less than 1.5×10-3 for 97.3% of the 1929 test set. For synthesizing the unit cell's design parameters, the MSE is less than 2.5×10-3 for 95.7% of the test set. The data-driven method is further extended to a generative adversarial network (GAN) to create the WPT paths and predict the frequency spectra of them. To achieve high efficiency, we propose a cost function focusing on the spectra's transmission peak. After training using 80000 measured data, the GAN can create WPT paths that efficiently connect the transmitter and the receiver on the metasurface. The results show that the DNN provides an alternative and efficient design method for the metamaterial, replacing traditional EM-simulation-based approaches. INDEX TERMS wireless power transfer, tunable metamaterial, metasurface, field localization, deep neural network, generative adversarial network.

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